






'''simple docstring'''







import argparse
import json
from pathlib import Path

import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download

from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase        =							logging.get_logger(__name__)


def       __UpperCamelCase					(      lowercase_       :  List[Any]   ):
       """simple docstring"""




       a_						     =			ASTConfig()

       if "10-10" in model_name:
              pass
       elif "speech-commands" in model_name:
              a_						     =			128
       elif "12-12" in model_name:
              a_						     =			12
              a_						     =			12
       elif "14-14" in model_name:
              a_						     =			14
              a_						     =			14
       elif "16-16" in model_name:
              a_						     =			16
              a_						     =			16
       else:
              raise ValueError('Model not supported'   )

       a_						     =			'huggingface/label-files'
       if "speech-commands" in model_name:
              a_						     =			35
              a_						     =			'speech-commands-v2-id2label.json'
       else:
              a_						     =			527
              a_						     =			'audioset-id2label.json'

       a_						     =			json.load(open(hf_hub_download(lowercase_   ,       lowercase_   ,       repo_type='dataset'   )   ,       'r'   )   )
       a_						     =			{int(lowercase_   ): v for k, v in idalabel.items()}
       a_						     =			idalabel
       a_						     =			{v: k for k, v in idalabel.items()}

       return config


def       __UpperCamelCase					(      lowercase_       :  List[Any]   ):
       """simple docstring"""




       if "module.v" in name:
              a_						     =			name.replace('module.v'   ,       'audio_spectrogram_transformer'   )
       if "cls_token" in name:
              a_						     =			name.replace('cls_token'   ,       'embeddings.cls_token'   )
       if "dist_token" in name:
              a_						     =			name.replace('dist_token'   ,       'embeddings.distillation_token'   )
       if "pos_embed" in name:
              a_						     =			name.replace('pos_embed'   ,       'embeddings.position_embeddings'   )
       if "patch_embed.proj" in name:
              a_						     =			name.replace('patch_embed.proj'   ,       'embeddings.patch_embeddings.projection'   )
       # transformer blocks
       if "blocks" in name:
              a_						     =			name.replace('blocks'   ,       'encoder.layer'   )
       if "attn.proj" in name:
              a_						     =			name.replace('attn.proj'   ,       'attention.output.dense'   )
       if "attn" in name:
              a_						     =			name.replace('attn'   ,       'attention.self'   )
       if "norm1" in name:
              a_						     =			name.replace('norm1'   ,       'layernorm_before'   )
       if "norm2" in name:
              a_						     =			name.replace('norm2'   ,       'layernorm_after'   )
       if "mlp.fc1" in name:
              a_						     =			name.replace('mlp.fc1'   ,       'intermediate.dense'   )
       if "mlp.fc2" in name:
              a_						     =			name.replace('mlp.fc2'   ,       'output.dense'   )
       # final layernorm
       if "audio_spectrogram_transformer.norm" in name:
              a_						     =			name.replace('audio_spectrogram_transformer.norm'   ,       'audio_spectrogram_transformer.layernorm'   )
       # classifier head
       if "module.mlp_head.0" in name:
              a_						     =			name.replace('module.mlp_head.0'   ,       'classifier.layernorm'   )
       if "module.mlp_head.1" in name:
              a_						     =			name.replace('module.mlp_head.1'   ,       'classifier.dense'   )

       return name


def       __UpperCamelCase					(      lowercase_       :  Tuple   ,       lowercase_       :  str   ):
       """simple docstring"""




       for key in orig_state_dict.copy().keys():
              a_						     =			orig_state_dict.pop(lowercase_   )

              if "qkv" in key:
                     a_						     =			key.split('.'   )
                     a_						     =			int(key_split[3]   )
                     a_						     =			config.hidden_size
                     if "weight" in key:
                            a_						     =			val[:dim, :]
                            a_						     =			val[dim : dim * 2, :]
                            a_						     =			val[-dim:, :]
                     else:
                            a_						     =			val[:dim]
                            a_						     =			val[dim : dim * 2]
                            a_						     =			val[-dim:]
              else:
                     a_						     =			val

       return orig_state_dict


def       __UpperCamelCase					(      lowercase_       :  Optional[Any]   ):
       """simple docstring"""




       a_						     =			[
           'module.v.head.weight',
           'module.v.head.bias',
           'module.v.head_dist.weight',
           'module.v.head_dist.bias',
       ]
       for k in ignore_keys:
              state_dict.pop(lowercase_   ,       lowercase_   )


@torch.no_grad()
def       __UpperCamelCase					(      lowercase_       :  str   ,       lowercase_       :  Dict   ,       lowercase_       :  Optional[Any]=False   ):
       """simple docstring"""




       a_						     =			get_audio_spectrogram_transformer_config(lowercase_   )

       a_						     =			{
           'ast-finetuned-audioset-10-10-0.4593': (
               'https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'
           ),
           'ast-finetuned-audioset-10-10-0.450': (
               'https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'
           ),
           'ast-finetuned-audioset-10-10-0.448': (
               'https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'
           ),
           'ast-finetuned-audioset-10-10-0.448-v2': (
               'https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'
           ),
           'ast-finetuned-audioset-12-12-0.447': (
               'https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'
           ),
           'ast-finetuned-audioset-14-14-0.443': (
               'https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'
           ),
           'ast-finetuned-audioset-16-16-0.442': (
               'https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'
           ),
           'ast-finetuned-speech-commands-v2': (
               'https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'
           ),
       }

       # load original state_dict
       a_						     =			model_name_to_url[model_name]
       a_						     =			torch.hub.load_state_dict_from_url(lowercase_   ,       map_location='cpu'   )
       # remove some keys
       remove_keys(lowercase_   )
       # rename some keys
       a_						     =			convert_state_dict(lowercase_   ,       lowercase_   )

       # load 🤗 model
       a_						     =			ASTForAudioClassification(lowercase_   )
       model.eval()

       model.load_state_dict(lowercase_   )

       # verify outputs on dummy input
       # source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
       a_						     =			-4.267_7393 if 'speech-commands' not in model_name else -6.84_5978
       a_						     =			4.568_9974 if 'speech-commands' not in model_name else 5.565_4526
       a_						     =			1_024 if 'speech-commands' not in model_name else 128
       a_						     =			ASTFeatureExtractor(mean=lowercase_   ,       std=lowercase_   ,       max_length=lowercase_   )

       if "speech-commands" in model_name:
              a_						     =			load_dataset('speech_commands'   ,       'v0.02'   ,       split='validation'   )
              a_						     =			dataset[0]['audio']['array']
       else:
              a_						     =			hf_hub_download(
                  repo_id='nielsr/audio-spectogram-transformer-checkpoint'   ,       filename='sample_audio.flac'   ,       repo_type='dataset'   ,       )

              a_     ,	a_						     =			torchaudio.load(lowercase_   )
              a_						     =			waveform.squeeze().numpy()

       a_						     =			feature_extractor(lowercase_   ,       sampling_rate=16_000   ,       return_tensors='pt'   )

       # forward pass
       a_						     =			model(**lowercase_   )
       a_						     =			outputs.logits

       if model_name == "ast-finetuned-audioset-10-10-0.4593":
              a_						     =			torch.tensor([-0.8760, -7.0042, -8.6602]   )
       elif model_name == "ast-finetuned-audioset-10-10-0.450":
              a_						     =			torch.tensor([-1.1986, -7.0903, -8.2718]   )
       elif model_name == "ast-finetuned-audioset-10-10-0.448":
              a_						     =			torch.tensor([-2.6128, -8.0080, -9.4344]   )
       elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
              a_						     =			torch.tensor([-1.5080, -7.4534, -8.8917]   )
       elif model_name == "ast-finetuned-audioset-12-12-0.447":
              a_						     =			torch.tensor([-0.5050, -6.5833, -8.0843]   )
       elif model_name == "ast-finetuned-audioset-14-14-0.443":
              a_						     =			torch.tensor([-0.3826, -7.0336, -8.2413]   )
       elif model_name == "ast-finetuned-audioset-16-16-0.442":
              a_						     =			torch.tensor([-1.2113, -6.9101, -8.3470]   )
       elif model_name == "ast-finetuned-speech-commands-v2":
              a_						     =			torch.tensor([6.1589, -8.0566, -8.7984]   )
       else:
              raise ValueError('Unknown model name'   )
       if not torch.allclose(logits[0, :3]   ,       lowercase_   ,       atol=1E-4   ):
              raise ValueError('Logits don\'t match'   )
       print('Looks ok!'   )

       if pytorch_dump_folder_path is not None:
              Path(lowercase_   ).mkdir(exist_ok=lowercase_   )
              print(F'Saving model {model_name} to {pytorch_dump_folder_path}'   )
              model.save_pretrained(lowercase_   )
              print(F'Saving feature extractor to {pytorch_dump_folder_path}'   )
              feature_extractor.save_pretrained(lowercase_   )

       if push_to_hub:
              print('Pushing model and feature extractor to the hub...'   )
              model.push_to_hub(F'MIT/{model_name}'   )
              feature_extractor.push_to_hub(F'MIT/{model_name}'   )


if __name__ == "__main__":
  __lowerCAmelCase        =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="ast-finetuned-audioset-10-10-0.4593",
      type=str,
      help="Name of the Audio Spectrogram Transformer model you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
  )

  __lowerCAmelCase        =							parser.parse_args()
  convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












'''simple docstring'''







import cmath
import math


def       __UpperCamelCase					(      lowercase_       :  float   ,       lowercase_       :  float   ,       lowercase_       :  float   ,       lowercase_       :  float   ):
       """simple docstring"""




       a_						     =			math.radians(lowercase_   )
       a_						     =			math.radians(lowercase_   )

       # Convert voltage and current to rectangular form
       a_						     =			cmath.rect(lowercase_   ,       lowercase_   )
       a_						     =			cmath.rect(lowercase_   ,       lowercase_   )

       # Calculate apparent power
       return voltage_rect * current_rect


if __name__ == "__main__":
  import doctest

  doctest.testmod()




